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Abstract
The paper proposes a new method for a kind of parametric fault online diagnosis with state estimation jointly. The considered fault affects not only the deterministic part of the system but also the random circumstance. The proposed method first applies Kalman Filter (KF) and Maximum Likelihood (ML) technique to identify the fault parameter and employs the result to make fault decision based on the predefined threshold. Then this estimated fault parameter value is substituted into parameterized state estimation of KF to obtain the state estimation. Finally, a robot case study with two different fault scenarios shows this method can lead to a good performance in terms of fast and accurate fault detection and state estimation.
Original language | English |
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Title of host publication | Proceedings of the 19th World Congress of the International Federation of Automatic Control, IFAC 2014 |
Number of pages | 6 |
Publisher | IFAC Publisher |
Publication date | 2014 |
Pages | 8293-8298 |
DOIs | |
Publication status | Published - 2014 |
Event | 19th World Congress of the International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa Duration: 24 Aug 2014 → 29 Aug 2014 |
Conference
Conference | 19th World Congress of the International Federation of Automatic Control, IFAC 2014 |
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Country/Territory | South Africa |
City | Cape Town |
Period | 24/08/2014 → 29/08/2014 |
Series | I F A C Workshop Series |
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ISSN | 1474-6670 |
Keywords
- Parameter Identification
- State Estimation
- Kalman Filter
- Maximum Likelihood
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Nonlinear system identification and its application to fault detection and diagnosis
Yang, Z. & Sun, Z.
01/11/2008 → 30/06/2013
Project: Research